Structuring Machine Learning Projects — When we build any type of project, there are checks that the project should accomplish. In the case of machine learning projects are nos distinct. Since now we have been explaining the mathematics(Statistics, Probability, Linear algebra, Calculus) that will allow us to understand how machine learning models work. …

Books and course recommendations — In this post, I try to explain how I became a data scientist/machine learning engineer based on books. Define your starting point Anyone, at any age, can acquire the skills to work in any data-related field, being data science, data engineering, data analyst, etc The first step is to define where do you start…

The origins of Deep Learning and Support Vector Machines — The separating hyperplanes procedure constructs linear decision boundaries that explicitly try to separate the data into different classes as well as possible. With them, we will define the Support Vector Classifier. Sometimes LDA and logistic regression explained in the previous post make avoidable errors, this can be solved using the…

Linear methods for classification — As we try to classificate our data into distinct groups, our predictor G(x) takes values in a discrete set ζ and we can divide the input space into a collection of regions labeled according to the classification. With linear methods, we mean that the decision boundaries between our predicted classes…

Tuning the basic linear regression model — In previous posts, we introduced least-squares and explained some subset selection techniques. Today we are here to introduce more subset selection and shrinkage models. Least angle regression(LAR) Least angle regression is a kind of forward stepwise that only enters as much of a predictor as it deserves. …

Comparing distinct linear regression subset selection methods Ridge, Lasso, and Enet applied. — In the last two posts, we have been explaining the theory of linear regression and some subset selection techniques. It’s been very math-focused, but now we know all that we need to apply them using python. To know how this application works, don’t miss the last two posts: Explaining least-squares. …

Tuning the basic linear regression model — In the last post, we explained the most used linear regression machine learning technique, the least-squares. We explained distinct approaches to multiple linear regressions and regressions with multiple outputs. But we assumed that we use all variables in the regression, today we will explain some techniques to select only a…

Formulas to accelerate integration — In this post, we will summarise some of the most useful techniques to calculate integrals. This will allow you to avoid the limit notation, but there will always be some difficult integrals. Primitive functions A function F satisfying F’ = f is called a primitive of f. …

The relation between derivatives and integrals — The first theorem relates derivation with the integration of functions, it is divided between two theorems, let’s explain them. The first fundamental theorem of calculus Let f be integrable on [a,b], and define F on [a,b] by